@Article{JICS-8-316,
author = {Cunlou Lu, Shangbing Gao and Zecheng Zhou},
title = {Maize Disease Recognition via Fuzzy Least Square Support Vector Machine},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {8},
number = {4},
pages = {316--320},
abstract = { In  this  paper,  we  propose  a  new  approach  to  recognize  the  maize  disease,  which  is  based  on 
fuzzy  least  square  vector  machine  (FLSVM)  algorithm.  According  to  the  texture  characteristics  of  Maize 
diseases, it uses YCbCr color space technology to segment disease spot, and uses the co-occurrence matrix 
spatial gray level layer to extract disease spot texture feature, and uses FLSVM to class the maize disease. In 
this method, the sample mean is calculated, and the center of each class is got; then the distance between the 
sample and the center is calculated, according to the distance sample’s initial membership is got; by finding 
K  neighbors  for  each  sample  point,  the  sample  membership  degree  is  calculated  according  to  the  fuzzy  K 
nearest neighbor method. Extensive experiments on public datasets show that the algorithm can effectively 
identify the disease image, the accuracy was as high as 98% or more. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22608.html}
}